SCOPE: Integrating Organoid Screening and Clinical Variables Through Machine Learning for Cancer Trial Outcome Prediction

BackgroundPredicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to fore-cast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform that integrates PDO drug screening with clinical prognostic modeling to predict arm-level median progression-free survival (mPFS) and objective response rate (ORR) without access to any trial outcome data. Patients and methodsSCOPE was trained on 54 treatment lines from 52 independent patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adeno-carcinoma (mPDAC, n=39) with matched clinical data and PDO drug screening across 9 compounds. A Clinical Score module captures baseline prognosis; a Drug Screen Score module quantifies treatment-specific organoid sensitivity. To predict trial outcomes, synthetic patient profiles are generated from published eligibility criteria and matched to a biobank of 81 PDO lines. Predictions were externally validated against 32 arms from 23 published trials, treatment ranking was assessed across 8 head-to-head comparisons, and prospective applicability was tested for Daraxonrasib (RMC-6236), a novel pan-RAS inhibitor in mPDAC. ResultsPredicted mPFS strongly agreed with published outcomes (R2=0.85, MAE=0.82 months; Pearson r=0.92, P <0.001), approaching the empirical concordance between two independently measured clinical endpoints (ORR vs. mPFS, R2=0.87). ORR prediction was similarly robust (R2=0.71, MAE=7.3 percentage points). Integrating organoid and clinical data significantly out-performed either alone (P =0.001). SCOPE correctly identified the superior arm in 7 of 8 head-to-head comparisons (88%, P <0.05). Applied to Daraxonrasib prior to phase 3 data availability, the platform predicted superiority over standard chemotherapy in KRAS-mutant mPDAC, consistent with emerging trial data. ConclusionBy combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior trial data. This approach could support clinical trial design, treatment arm selection, and go/no-go decisions, offering a new tool to improve the efficiency of gastrointestinal cancer drug development.